SavedAdd your journal

Journals / Avrupa Bilim ve Teknoloji Dergisi / 2026 / Özel Sayı

Classification of Sleep Stages Using PSG Recording Signals

PSG Kayıt Sinyalleri Kullanılarak Uyku Evrelerinin Sınıflandırılması

Yasin Koca(KONYA TEKNİK ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ)0000-0003-0064-8555
Seral Özşen(MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0001-5332-8665
Fatma Zehra Göğüş(KONYA TEKNİK ÜNİVERSİTESİ)0000-0001-5035-7575
Gülay Tezel(KONYA TEKNİK UNİVERSİTESIİ)0000-0003-1698-0106
Serkan Küçüktürk(NECMETTİN ERBAKAN ÜNİVERSİTESİ)0000-0001-8445-666X
Hülya Vatansev(NECMETTİN ERBAKAN ÜNİVERSİTESİ)0000-0002-8382-3904
Pages
315–321
Publication Date
2020-10-05
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Automatic sleep staging is aimed within the scope of this paper.
📊
Key Findings & Data:In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment.
💡
Conclusion & Impact:Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:PSG · Sleep Stages · EEG

Abstract

Automatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.

Özet

Automatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.

🔖

Cite This Article

Koca, Y., Özşen, S., Göğüş, F. Z., Tezel, G., Küçüktürk, S., Vatansev, H. (2020). Classification of Sleep Stages Using PSG Recording Signals. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 315–321. https://doi.org/10.31590/ejosat.804709

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Abdulla, S., Diykh, M., Laft, R. L., Saleh, K., & Deo, R. C. (2019). Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm. Expert Systems With Applications, vol. 138. DOI:https://doi.org/10.1016/j.eswa.2019.07.007
  2. Diykh, M., Li, Y., & Abdulla, S. (2020). EEG sleep stages identification based on weighted undirected complex networks. Computer Methods and Programs in Biomedicine, vol. 184. DOI: https://doi.org/10.1016/j.cmpb.2019.105116.
  3. Fan, Y. (2018). Research on Feature Extraction of EEG Signals using MSE-PCA and Sleep Staging. 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Qingdao, pp. 1-5, DOI:10.1109/ICSPCC.2018.8567757.
  4. Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology, vol. 29, issue. 3, pp. 306–310. DOI: 10.1016/0013-4694(70)90143-4
  5. Huang, W., Guo, B., Shen, Y., Tang, X., Zhang, T., Li, D., & Zhonghui J. (2020). Sleep staging algorithm based on multichannel data adding and multifeature screening. Computer Methods and Programs in Biomedicine, vol.187. DOI: https://doi.org/10.1016/j.cmpb.2019.105253.
  6. Iber, C., Ancoli-Israel, S., Chesson, A. L., & Quan, S. L. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester, 2007.
  7. Jiang, D., Lu, Y., Ma, Y., & Wang, Y. (2019). Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement. Expert Systems With Applications, vol. 121, pp. 188–203. DOI:https://doi.org/10.1016/j.eswa.2018.12.023
  8. Liu, G. R., Lo, Y. L., Malik, J., Sheu, Y. C., & Wu, H. T. (2020). Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification. Biomedical Signal Processing and Control, vol. 55. DOI: https://doi.org/10.1016/j.bspc.2019.101576.
  9. Savareh, B. A., Bashiri, A., Behmanesh, A., Meftahi, G. H., & Hatef, B. (2018). Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ. DOI 10.7717/peerj.5247
  10. Smith, L. I. (2002). A tutorial on Principal Components Analysis. http://www.sccg.sk/~haladova/principal_components.pdf
  11. Welch, P. D. (1967). The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Trans. Audio Electroacoust, vol. AU-15 (June 1967). pp. 70-73. DOI:10.1109/TAU.1967.1161901
  12. Whitney, A. W. (1971). A Direct Method of Nonparametric Measurement Selection. IEEE Transactions on Computers, vol. C-20, issue. 9, pp. 1100-1103. DOI: 10.1109/T-C.1971.223410
  13. Yücelbaş,Ş., Yücelbaş, C., Özşen, S., Tezel, G., Dursun, M., Küçüktürk, S., & Yosunkaya, Ş. (2015). Effect On The Classification Results of ECG Artifacts in Full Night Sleep EEG. The International Conference On Science, Ecology And Technology I, ICONSETE 2015, Vienna, Austria.
  14. Huang, W., Guo, B., Shen, Y., Tang, X., Zhang, T., Li, D., & Zhonghui J. (2020). Sleep staging algorithm based on multichannel data adding and multifeature screening. Computer Methods and Programs in Biomedicine, vol.187. DOI: https://doi.org/10.1016/j.cmpb.2019.105253.
  15. Savareh, B. A., Bashiri, A., Behmanesh, A., Meftahi, G. H., & Hatef, B. (2018). Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ. DOI 10.7717/peerj.5247